From Documentation to AI Insights: A RAG Chatbot for LangGraph and LangChain

Overview:
This project aims to build a Retrieval-Augmented Generation (RAG) chatbot that leverages documentation from LangChain and LangGraph to provide contextual AI-driven insights. The chatbot will allow users to ask natural language questions and retrieve precise answers from documentation.

Objectives:

• Enable AI-driven access to LangChain & LangGraph documentation.
• Implement RAG pipeline using a vector database.
• Demonstrate integration of LangChain and LangGraph for knowledge retrieval.

Approach:

1. Collect documentation and knowledge base.
2. Preprocess documents and convert them into embeddings.
3. Use a vector database to store and query embeddings.
4. Implement RAG chatbot to answer user queries using OpenAI GPT models.

Expected Outcome:
A functional chatbot that accurately retrieves answers from documentation and supports developers or learners in understanding LangChain and LangGraph concepts.

Methodology

Results